Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery

In the mid-1800s, tile-drains were installed in poorly-drained soils of topographic lows as water management to protect cropland during wet conditions; consequently, estimations of tile-drain location have been based on soil series. Most tile drains are in the Midwest, however each state has farms with tile and tile-drain density has increased in the last decade. Where tile drains quickly remove water from fields, groundwater and stream water interaction can change, affecting water availability and flooding. Nutrients and sediment can quickly travel to streams thru tile, contributing to harmful algal blooms and hypoxia in large water bodies. Tile drains are below the soil surface, about 1 m deep, but their location can be visible in satellite imagery as patterns in soil or plant color. We will develop a machine-learning approach to: (1) identify satellite imagery with visible tile drains; (2) differentiate topographic-soil tiles from densely-patterned tile that extends to new areas.



Principal Investigator : Tanja N Williamson

Co-Investigator : Alexander O Headman

Cooperator/Partner : Michael E Wieczorek, Barry Allred